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Computational Statistics & Data Analysis COMPUTATIONAL STATISTICS & DATA ANALYSIS AUTHOR INFORMATION PACK TABLE OF CONTENTS XXX . • Description p.1 • Audience p.2 • Impact Factor p.2 • Abstracting and Indexing p.2 • Editorial Board p.2 • Guide for Authors p.7 ISSN: 0167-9473 DESCRIPTION . Computational Statistics and Data Analysis (CSDA), an Official Publication of the network Computational and Methodological Statistics (CMStatistics) and of the International Association for Statistical Computing (IASC), is an international journal dedicated to the dissemination of methodological research and applications in the areas of computational statistics and data analysis. The journal consists of four refereed sections which are divided into the following subject areas: I) Computational Statistics - Manuscripts dealing with: 1) the explicit impact of computers on statistical methodology (e.g., Bayesian computing, bioinformatics,computer graphics, computer intensive inferential methods, data exploration, data mining, expert systems, heuristics, knowledge based systems, machine learning, neural networks, numerical and optimization methods, parallel computing, statistical databases, statistical systems), and 2) the development, evaluation and validation of statistical software and algorithms. Software and algorithms can be submitted with manuscripts and will be stored together with the online article. II) Statistical Methodology for Data Analysis - Manuscripts dealing with novel and original data analytical strategies and methodologies applied in biostatistics (design and analytic methods for clinical trials, epidemiological studies, statistical genetics, or genetic/environmental interactions), chemometrics, classification, data exploration, density estimation, design of experiments, environmetrics, education, image analysis, marketing, model free data exploration, pattern recognition, psychometrics, statistical physics, image processing, robust procedures. Statistical methodology includes, but not limited to: bootstrapping, classification techniques, clinical trials, data exploration, density estimation, design of experiments, pattern recognition/image analysis, parametric and nonparametric methods, statistical genetics, Bayesian modeling, outlier detection, robust procedures, cross-validation, functional data, fuzzy statistical analysis, mixture models, model selection and assessment, nonlinear models, partial least squares, latent variable models, structural equation models, supervised learning, signal extraction and filtering, time-series modelling, longitudinal analysis, multilevel analysis and quality control. III) Special Applications - Manuscripts at the interface of statistics and computing (e.g., comparison of statistical methodologies, computer-assisted instruction for statistics, simulation experiments). Advanced statistical analysis with real applications (social sciences, marketing, psychometrics, chemometrics, signal processing, medical statistics, environmentrics, statistical physics). AUTHOR INFORMATION PACK 23 Sep 2021 www.elsevier.com/locate/csda 1 IV) Annals of Statistical Data Science - The manuscripts concern with well-founded theoretical and applied data-driven research, with a significant computational or statistical methodological component for data analytics. Emphasis is given to comprehensive and reproducible research, including data- driven methodology, algorithms and software. AUDIENCE . Statisticians (university, government, business, industry), Statistical Software users. IMPACT FACTOR . 2020: 1.681 © Clarivate Analytics Journal Citation Reports 2021 ABSTRACTING AND INDEXING . ACM Computing Reviews Engineering Index Current Index to Statistics Mathematical Reviews Statistical Theory and Method Abstracts INSPEC QCAS OR/MS CompuMath Citation Index Research Alert Science Citation Index Expanded Zentralblatt MATH Science Citation Index Scopus EDITORIAL BOARD . Co-Editors Ana Maria Colubi, University of Giessen, 35390, Gießen, Germany Erricos Kontoghiorghes, Birkbeck University of London, WC1E 7HX, London, United Kingdom and Cyprus University of Technology, Malet Street, 3036, Lemesos, Cyprus Byeong Park, Seoul National University Department of Statistics, 1 Gwanak-ro, Gwanak-gu, 08826, Seoul, South Korea Advisory Board Peter Bühlmann, ETH Zurich, Zurich, Switzerland Statistics, machine learning, computational biology Manfred Gilli, University of Geneva, Geneva, Switzerland Numerical Methods with Applications to Statistics and Econometric; Algorithms for Statistical Model Selection Techniques; Heuristic Optimization Methods in Statistics; and High Performance Computing Jae Chang Lee, Korea University Department of Statistics, Seongbuk-gu, South Korea Nonparametric change point problems, multivariate categorical analysis, data matching and classification tree methods Joyce Niland, City of Hope, Duarte, California, United States of America (Managing Co-Editor - IASC News) Stephen Pollock, Queen Mary University of London, London, United Kingdom Statistical analysis in the frequency domain, filtering methods, wavelets, econometric methods, time series analysis, functional analysis Jane-Ling Wang, University of California Davis, Davis, California, United States of America Dimension reduction methods, Functional data analysis, Longitudinal data analysis, Survival analysis, Joint modeling of survival/longitudinal data and neuroimaging data AUTHOR INFORMATION PACK 23 Sep 2021 www.elsevier.com/locate/csda 2 Associate Editors Julyan Arbel, Inria Research Centre Grenoble Rhône-Alpes, Montbonnot St Martin, France Bayesian Statistics and Machine Learning with applications Andreas Artemiou, Cardiff University School of Mathematics, Dimension Reduction, High-dimensional Statistics, Kernel methods, Machine Learning, Regression, Classification, , , , Eric Beutner, VU Amsterdam, Amsterdam, Netherlands Dependent data, Differentiability in statistics, Empirical processes, Non- and semi-parametric methods in reliability/survival analysis, Statistical functionals Enea Bongiorno, University of Eastern Piedmont, Vercelli, Italy Small Ball Probability, Functional Statistics, High dimensional data Antonio Canale, University of Padua, Padova, Italy Bayesian computation, Bayesian inference, Bayesian nonparametrics, mixture models, skew distributions Eva Cantoni, University of Geneva, Geneva, Switzerland Robust statistics, generalized linear and additive (mixed) models, zero-inflated and zero-altered models, variable / model selection Miguel de Carvalho, The University of Edinburgh, Edinburgh, United Kingdom Bayesian nonparametrics; Biometrics and medical statistics; Data science and statistical learning; Statistics of extremes; Visualization and graphical methods Stefano Castruccio, University of Notre Dame, Notre Dame, Indiana, United States of America Spatio-temporal statistics, environmental statistics, global models, spectral methods, time series, climate model emulation Shih-Kang Chao, University of Missouri, Columbia, Missouri, United States of America Statistical Procedures for big data, quantile regression, high-dimensional model inference, statistical inference for weakly dependent data (with high dimensionality) Ray-bin Chen, National Cheng Kung University, Tainan, Taiwan Experimental design, computer experiment, machine learning, Bayesian variable selection Taeryon Choi, Korea University Department of Statistics, Seongbuk-gu, South Korea Bayesian methods, Bayesian hierarchical models, Bayesian model selection, Bayesian nonparametric inference, Non/Semiparametric regression models Bertrand Clarke, University of Nebraska-Lincoln, Lincoln, Nebraska, United States of America Data mining and machine learning, prediction,statistical techniques for complex or high-dimensional data, model bias and uncertainty Radu Craiu, University of Toronto, Toronto, Ontario, Canada Bayesian inference, Copula modelling, Markov chain Monte Carlo, Model choice, Statistical Genetics Christophe Croux, EDHEC Business School, Roubaix, France Data science in business and economics, Time series analysis, Outlier Detection Fabrizio Durante, University of Salento, Lecce, Italy Dependence Models, Copulas, Multivariate Analysis Takeshi Emura, Kurume University, Kurume, Japan Copulas, Cox regression, Dependent censoring/truncation, Double-truncation, Dynamic prediction, Meta-analysis, Multivariate survival analysis, High-dimensional data analysis, Joint models, Survival prediction Maria Brigida Ferraro, University of Rome La Sapienza, Roma, Italy Clustering and Classification, Imprecise Data, Bootstrap Frédéric Ferraty, Paul Sabatier University, Toulouse, France Functional data analysis, high dimensional data, non/semi-parametric modelling, model selection, theory and practice Cristian Gatu, Alexandru Ioan Cuza University, Iasi, Romania Algorithms for model selection, Combinatorial Algorithms, Parallel Computing Armelle Guillou, University of Strasbourg, Strasbourg, France Bootstrap methods, extreme value, censoring Michele Guindani, University of California Irvine, Irvine, California, United States of America Bayesian Analysis, Bayesian Nonparametrics, Biostatistics, Statistical decision making, multiple hypotheses testing Xuming He, University of Michigan, Ann Arbor, Michigan, United States of America Robust statistics, quantile regression, subgroup analysis, model selection Daniel Henderson, Newcastle University, Newcastle Upon Tyne, United Kingdom Bayesian inference and computation, Rank ordered data, Mixture models, computer models, Model selection, General applications including sports analytics
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